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Induced-fit Model01:13

Induced-fit Model

Most chemical reactions in cells require enzymes—biological catalysts that speed up the reaction without being consumed or permanently changed. They reduce the activation energy needed to convert the reactants into products. Enzymes are proteins, that usually work by binding to a substrate—a reactant molecule that they act upon.
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Related Experiment Video

Updated: May 19, 2026

GENPLAT: an Automated Platform for Biomass Enzyme Discovery and Cocktail Optimization
11:38

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Published on: October 24, 2011

MAPLE: a machine-learning force-field-native platform for automated reaction modeling and enzyme design.

Xujian Wang1,2,3, Zeyu Sun1, Yilu Zhang1

  • 1Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh Pittsburgh Pennsylvania 15261 USA junmei.wang@pitt.edu.

Chemical Science
|May 18, 2026
PubMed
Summary

We developed MAPLE, a machine learning potential for landscape exploration toolkit, to enable accurate and efficient molecular modeling. This platform accelerates catalyst design and drug discovery by integrating advanced machine learning force fields.

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Area of Science:

  • Computational chemistry and biology
  • Molecular modeling

Background:

  • Machine-learning force fields (MLFFs) offer near-quantum mechanical accuracy at reduced computational cost.
  • Existing MLFF applications are limited by the lack of unified, automated computational platforms.

Purpose of the Study:

  • To introduce MAPLE (MAchine learning Potential for Landscape Exploration), a novel computational toolkit for MLFF-based molecular modeling.
  • To provide a unified and automated platform for large-scale and versatile molecular modeling tasks.

Main Methods:

  • Developed a tailored software framework and parallelized algorithms for MLFF applications.
  • Systematically benchmarked state-of-the-art reactive MLFFs within the MAPLE framework.
  • Applied MAPLE to simulate multiple biocatalytic scenarios.

Main Results:

  • Demonstrated the robustness and usability of the MAPLE toolkit.
  • Highlighted MAPLE's capability for fast and accurate simulation of catalytic reactions.
  • Validated MAPLE's performance through systematic benchmarking and application studies.

Conclusions:

  • MAPLE integrates accurate MLFFs with parallelized algorithms in an optimized framework.
  • MAPLE serves as a next-generation, machine-learning-driven molecular modeling platform.
  • MAPLE has broad applicability in rational catalyst design and drug discovery.